TY - JOUR T1 - Robust variable selection for mixture linear regression models AU - Jiang, Yunlu PY - 2016 DA - April JF - Hacettepe Journal of Mathematics and Statistics PB - Hacettepe University WT - DergiPark SN - 2651-477X SP - 549 EP - 559 VL - 45 IS - 2 LA - en AB - In this paper, we propose a robust variable selection to estimate and select relevant covariates for the finite mixture of linear regression modelsby assuming that the error terms follow a Laplace distribution to thedata after trimming the high leverage points. We introduce a revisedExpectation-maximization (EM) algorithm for numerical computation.Simulation studies indicate that the proposed method is robust to boththe high leverage points and outliers in the y-direction, and can obtaina consistent variable selection in the case of outliers or heavy-tail errordistribution. Finally, we apply the proposed methodology to analyze areal data. 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